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theses

Facilitating use of sensitive data for research or commercial purposes, in a manner that preserves the privacy of participating entities, is an active area of study. Differential privacy is a popular, relatively recent, framework that formalizes data privacy. In this dissertation, I examine the often conflicting goals of privacy and utility within the framework of differential privacy. The contributions of this dissertation fall into two main categories: 1) We propose differentially private algorithms for several tasks that could potentially involve sensitive data, such as synthetic graph modeling, human mobility modeling using cellular phone data, regression, and computing statistics on online data. We study the tradeoff between privacy and utility for these analyses—theoretically in some cases, and experimentally in others. We show that for each of these tasks, both privacy and utility can be successfully achieved by considering a meaningful tradeoff between the two. 2) We also examine connections between information theory and differential privacy, demonstrating how differential privacy arises out of a tradeoff between information leakage and utility. We show that differentially private mechanisms arise out of minimizing the information leakage (measured using mutual information) under the constraint of achieving a given level of utility. Further, we establish a connection between differentially private learning and PAC-Bayesian bounds.

ETD_5061

doi:10.7282/T3FT8J2Z

Ph.D.

Includes bibliographical references

by Darakhshan J. Mir

ETD doctoral

The author owns the copyright to this work.